Occurrence of cryptosporidiosis has been associated with weather conditions in many settings internationally. We explored statistical clusters of human cryptosporidiosis and their relationship with severe weather events in New Zealand (NZ). Notified cases of cryptosporidiosis from 1997 to 2015 were obtained from the national surveillance system. Retrospective space–time permutation was used to identify statistical clusters. Cluster data were compared to severe weather events in a national database. SaTScan analysis detected 38 statistically significant cryptosporidiosis clusters. Around a third (34.2%, 13/38) of these clusters showed temporal and spatial alignment with severe weather events. Of these, nearly half (46.2%, 6/13) occurred in the spring. Only five (38%, 5/13) of these clusters corresponded to a previously reported cryptosporidiosis outbreak. This study provides additional evidence that severe weather events may contribute to the development of some cryptosporidiosis clusters. Further research on this association is needed as rainfall intensity is projected to rise in NZ due to climate change. The findings also provide further arguments for upgrading the quality of drinking water sources to minimize contamination with pathogens from runoff from livestock agriculture.

Johnsongrass [Sorghum halepense (L.) Pers.] is one of the most problematic perennial grass weed species in row-crop production across the southern United States. Control of this species is especially challenging in organic systems due to a lack of effective options. A field experiment was conducted at the Texas A&M research farm near College Station, TX, from fall 2019 to spring 2021 to evaluate various nonchemical options for managing S. halepense in the fallow season, implemented over 2 yr in the same locations. The treatments included disking once, disking twice, disking + immediate flooding, disking + flush irrigation + flooding, disking twice + flooding after the first frost, periodic mowing, acetic acid treatment, and disking + tarping. Disking + immediate flooding, disking + flush irrigation + flooding, and disking + tarping were the most effective treatments. Compared with the nontreated control plots, these treatments reduced S. halepense aboveground density (<9 plants m−2 vs. 64 plants m−2), aboveground biomass (<80 g m−2 vs. 935 g m−2), rhizome biomass (<4 g m−2 vs. 55 g m−2), rhizome node number (<25 nodes m−2 vs. 316 nodes m−2), and rhizome length (<42 cm m−2 vs. 660 cm m−2). Disking twice + flooding after the first frost did not show a consistent impact. Periodic mowing also reduced S. halepense density (12 plants m−2 vs. 64 plants m−2) and other variables compared with the control plots at the end of the study in spring 2021. Disking alone once or twice each growing season or repeated application of acetic acid failed to control S. halepense. These results indicate that well-timed nonchemical management practices such as tarping and flooding implemented during the winter fallow can be very effective in reducing S. halepense densities.

Extreme precipitation events are occurring more intensely in Canada. This can contaminate water sources with enteric pathogens, potentially increasing the risk of acute gastrointestinal illness. This study aimed to investigate the relationship between extreme precipitation and emergency department (ED) visits for acute gastrointestinal illness in Toronto from 2012 to 2022. Distributed lag non-linear models were constructed on ED visit counts with a Quasi Poisson distribution. Extreme precipitation was modelled as a 21-day lag variable, with a linear relationship assumed at levels ≧95th percentile. Separate models were also conducted on season-specific data sets. Daily precipitation and gastrointestinal illness ED visits ranged between 0 to 126 mm, and 12 to 180 visits respectively. Overall, a 10-mm increase in precipitation >95th percentile had no significant relationship with the risk of ED visits. However, stratification by seasons revealed significant relationships during spring (lags 1–19, peak at lag 14 RR = 1.04; 95% CI: 1.03, 1.06); the overall cumulative effect across the 21-day lag was also significant (RR = 1.94; 95% CI: 1.47, 2.57). Extreme precipitation has a seasonal effect on gastrointestinal health outcomes in Toronto city, suggesting varying levels of enteric pathogen exposures through drinking water or other environmental pathway during different seasons.

The potential benefits of providing digital mental healthcare to isolated rural populations are emphasised in two articles from Pakistan. Novel programmes of support have been instituted by both private and publicly funded services.

This article examines the problem of flooding in colonial Tonkin through two interrelated lenses: the history of disasters as social and political phenomena, and the history of technology and the constraints that shape its use. With a gradient ten times steeper than the Mekong, the Red River (Sông Cái in Vietnamese) is notorious for its huge seasonal fluctuations and violent floods. For centuries, local rulers and cultivators constructed dikes to protect fields and settlements, though breaches and inundations were frequent. French administrators were convinced that they could improve the flooding situation with modern know-how. From the 1890s, colonial engineers carefully studied the river's behavior, examined a range of different schemes to control it, and debated the extent to which the straitjacketing of the river might gradually exacerbate flood risk. Despite their deep-seated misgivings about the problems caused by dikes, they were ultimately forced to work within the parameters of pre-colonial hydraulic works. The result was an intensification of existing dependencies and flood vulnerabilities, which finally came to a head under the combined pressures of extreme weather and war, and which ultimately played an important role in undermining colonial authority in the Red River delta.

Pakistan has suffered heavy losses due to the torrential monsoon rains of 2022. With obliterated infrastructure and rising disease burden, the nation is still reeling from the dismal aftermath. It is critical to understand that such catastrophes are not a 1-time calamity but are likely to become more frequent with growing severity of the climate crisis. These losses point to a more systemic problem that is a lack of preparedness, and without sustainable long-term measures in place, the nation remains just as vulnerable to the next ‘unpredictable’ weather contingency. Prior planning and effective allocation of resources can help develop a proactive response to future disasters of this magnitude.

Monitoring river water levels is essential for the study of floods and mitigating their risks. River gauges are a well-established method for river water-level monitoring but many flood-prone areas are ungauged and must be studied through gauges located several kilometers away. Taking advantage of river cameras to observe river water levels is an accessible and flexible solution but it requires automation. However, current automated methods are only able to extract uncalibrated river water-level indexes from the images, meaning that these indexes are relative to the field of view of the camera, which limits their application. With this work, we propose a new approach to automatically estimate calibrated river water-level indexes from images of rivers. This approach is based on the creation of a new dataset of 32,715 images coming from 95 river cameras in the UK and Ireland, cross-referenced with gauge data (river water-level information), which allowed us to train convolutional neural networks. These networks are able to accurately produce two types of calibrated river water-level indexes from images: one for continuous river water-level monitoring, and the other for flood event detection. This work is an important step toward the automated use of cameras for flood monitoring.

Alkali barnyardgrass [Echinochloa crus-galli var. zelayensis (Kunth) Hitchc] and junglerice [Echinochloa colona (L.) Link] are problematic annual weeds in direct-seeded rice (Oryza sativa L.) fields in China. The emergence ecology of the two weed species may differ in response to environmental factors. Laboratory and screenhouse experiments were conducted to evaluate the effects of light, burial depth, mulching with wheat (Triticum aestivum L.) residue, and time and depth of flooding on the emergence of the two weed species collected from Nanjing, China. Light strongly increased seed germination. Under dark conditions, E. crus-galli seed germination (85%) was higher than that of E. colona (70%). The seeds of both species exhibited the greatest germination (90% for E. crus-galli and 80% for E. colona) when sown on the soil surface, and emergence decreased with increasing soil burial depth. Burial depths of 2.2 and 1.4 cm reduced seedling emergence by 50% for E. crus-galli and E. colona, respectively. No emergence was found at a depth of 6 cm. The seedling emergence for E. colona was lower than for E. crus-galli at the same soil burial depth. Mulching with wheat residue considerably reduced the seedling emergence and aboveground biomass of both species. The inhibitory effect of mulching with wheat residue on E. colona was more notable than on E. crus-galli. Early and deep flooding significantly suppressed the emergence, height, and biomass of E. crus-galli and E. colona, especially E. colona. The results gained from this study could provide fundamental ecological knowledge for managing Echinochloa species in direct-seeded rice systems.